Information and image processing through bio-inspired oscillatory cellular nonlinear networks

Many studies in neuroscience have shown that nonlinear dynamic networks represent a bio-inspired models for information and image processing. Recent studies on the thalamocortical system have shown that weakly connected oscillatory networks, forced by an external input, have the capability of modelling the architecture of a neurocomputer. In particular they have associative properties and can be exploited for dynamic pattern recognition. In this manuscript the global dynamic behavior of such networks is investigated. In case of weak coupling, their main dynamic features are revealed by the phase deviation equation (i.e. the equation that describes the phase deviation due to the weak coupling). Firstly a very accurate analytic expression of the phase deviation equation is derived, via the Joint application of the describing function technique and of Malkin's Theorem. Furthermore, a complete analysis of the phase-deviation equation shows that the proposed technique can be effectively exploited for designing dynamic associative memories.